The present invention is in the field of prediction and control of neurological disturbances, particularly in the area of electrographic and clinical seizure onset prediction based on implantable devices with the major goal of alerting and/or avoiding seizures.
Approximately 1% of the world's population has epilepsy, one third of whom have seizures not controlled by medications. Some patients, whose seizures reliably begin in one discrete region, usually in the mesial (middle) temporal lobe, may be cured by epilepsy surgery. This requires removing large volumes of brain tissue, because of the lack of a reliable method to pinpoint the location of seizure onset and the pathways through which seizures spread. The 25% of refractory patients in whom surgery is not an option must resort to inadequate treatment with high doses of intoxicating medications and experimental therapies, because of poorly localized seizure onsets, multiple brain regions independently giving rise to seizures, or because their seizures originate from vital areas of the brain that cannot be removed. For these and all other epileptic patients, the utilization of a predicting device would be of invaluable help. It could prevent accidents and allow these patients to do some activities that otherwise would be risky.
Individuals with epilepsy suffer considerable disability from seizures and resulting injuries, impairment of productivity, job loss, social isolation associated with having seizures, disabling side effects from medications and other therapies. One of the most disabling aspects of epilepsy is that seizures appear to be unpredictable. However, in this invention a seizure prediction system is disclosed. Seizure prediction is a highly complex problem that involves detecting invisible and unknown patterns, as opposed to detecting visible and known patterns involved in seizure detection. To tackle such an ambitious goal, some research groups have begun developing advanced signal processing and artificial intelligence techniques. The first natural question to ask is in what ways the preictal (i.e., the period preceding the time that a seizure takes place) intracranial EEGs (IEEGs) are different from all other IEEGs segments not immediately leading to seizures. When visual pattern recognition is insufficient, quantitative EEG analysis may help extract relevant characteristic measures called features, which can then be used to make statistical inferences or to serve as inputs in automated pattern recognition systems.
Typically, the study of an event involves the goals of diagnosing (detecting) or prognosticating (predicting) such event for corrective or preventive purposes, respectively. Particularly, in the case of brain disturbances such as epileptic seizures, these two major goals have driven the efforts in the field. On one hand, there are several groups developing seizure detection methods to implement corrective techniques to stop seizures, and on the other, there are some groups investigating seizure prediction methods to provide preventive ways to avoid seizures. Among the groups claiming seizure prediction, three categories of prediction can be distinguished, clinical onset (CO) prediction, electrographic onset (EO) prediction studies, and EO prediction systems. All these categories in conjunction with seizure detection compose most of the active research in this field.
Related art approaches have focused on nonlinear methods such as studying the behavior of the principal Lyapunov exponent (PLE) in seizure EEGs, computing a correlation dimension or nonlinear chaotic analysis or determining one major feature extracted from the ictal characteristics of an electroencephalogram (EEG) or electrocorticogram (ECoG).
Important Terminology Definitions
Ictal period: time when the seizure takes place and develops.
Preictal period: time preceding the ictal period.
Interictal period or baseline: period at least 1 hour away from a seizure. Note that the term baseline is generally used to denote “normal” periods of EEG activity, however, in this invention it is used interchangeably with interictal period.
Clinical onset (CO): the time when a clinical seizure is first noticeable to an observer who is watching the patient.
Unequivocal Clinical onset (UCO): the time when a clinical seizure is unequivocally noticeable to an observer who is watching the patient.
Unequivocal Electrographic Onset (UEO): also called in this work electrographic onset (EO), indicates the unequivocal beginning of a seizure as marked by the current “gold standard” of expert visual analysis of the IEEG.
Earliest Electrographic Change (EEC): the earliest change in the intracranial EEG (IEEG) preceding the UEO and possibly related to the seizure initiation mechanisms.
Focus Channel: the intracranial EEG channel where the UEO is first observed electrographically.
Focal Adjacent Channel: the intracranial EEG channels adjacent to the focus channel.
Focus Region: area of the brain from which the seizures first originate.
Feature: qualitative or quantitative measure that distills preprocessed data into relevant information for tasks such as prediction and detection.
Feature library: collection of algorithms used to determine the features.
Feature vector: set of selected features used for prediction or detection that forms the feature vector.
Aura: symptom of a brain disturbance usually preceding the seizure onset that may consist of hallucinations, visual illusions, distorted understanding, and sudden, intense emotion, such as anxiety or fear.
FIGS. 11A–11B illustrate some of the defined terms on segments of a raw IEEG signal. Comparison between the preictal segment indicated on FIG. 11A (between the EEC and the UEO times) and the interictal period in FIG. 11B demonstrates the difficulty of discerning between them. The vertical scale in both figures is in microvolts (μV).